Context-Specific Report Generation for Financial Data
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Solution Overview
Problem
Natural language generation systems face challenges in generating reports that are tailored to specific user contexts, leading to inefficiencies in data analysis and presentation, particularly in handling vast amounts of financial market data where user interest is not always aligned with the generated reports.
Innovation Solution
A method and apparatus that allow users to specify contextual information such as subject matter, time constraints, and data attributes through an interface, enabling the generation of context-specific reports that include natural language text and graphic displays, with interactive features like mouse-over annotations and hyperlinks, to focus on relevant data and reduce unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If natural language generation systems generate comprehensive reports from vast amounts of data, then the completeness of information is improved, but the relevance to user interests deteriorates
Solution Approach 1:
The system performs preliminary actions by collecting context information about user interests, preferences, and requirements before generating the report. This allows the system to pre-filter and prioritize data according to user-specific needs, ensuring both completeness of relevant information and relevance to user interests without requiring post-generation modifications
Solution Approach 2:
The system applies local quality by differentiating the treatment of different data elements based on their relevance to specific user contexts. Instead of uniformly processing all data, the system selectively emphasizes or de-emphasizes certain information based on user preferences, time constraints, and subject matter interests, thereby achieving both completeness and relevance simultaneously
2Loss of information
If the system processes all available data to ensure comprehensive reporting, then the completeness of the report is improved, but the data processing time increases
Solution Approach 1:
The system extracts only the necessary data elements required for generating a comprehensive report based on user context. By identifying and removing irrelevant data before processing, the system maintains completeness of essential information while significantly reducing the volume of data that requires processing, thereby decreasing processing time without sacrificing report quality
Solution Approach 2:
The system applies partial action by processing only the subset of data that is necessary to achieve comprehensive reporting for the specific user context. Rather than processing all available data excessively, the system determines the optimal scope of processing based on context information, ensuring completeness while avoiding unnecessary processing time expenditure
3Loss of information
If the report includes detailed information for all users, then the comprehensiveness is improved, but the ease of operation deteriorates
Solution Approach 1:
The system applies dynamics by making the report structure and content adaptable based on user context. The report dynamically adjusts its level of detail, organization, and emphasis according to user preferences, time constraints, and subject matter interests. This allows the report to maintain comprehensiveness while automatically optimizing its presentation for ease of operation for each specific user
Solution Approach 2:
The system changes parameters such as report length, detail level, and information prioritization based on user context. By adjusting these parameters according to user preferences and requirements, the system maintains comprehensive information while presenting it in a format that is optimized for ease of operation and user understanding
Data Source
AI summary
Example methods include converting received context information into a query to retrieve relevant data from a data repository, wherein the received context information defines a feature set. The method further includes retrieving a data set from the data repository, wherein the data set comprises data corresponding to the query. The example methods further include generating a set of messages that describe at least one linguistically describable trend in the data set, wherein the set of messages is instantiated based at least in part on the data set. The example methods further include generating a context-specific report about the feature set A corresponding apparatus and computer program product are provided.