Historical Data Analytics With Cached Reports and Predictive Queries

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Solution Overview

Problem

Conventional data analytic techniques for large collections of historical data are resource-intensive and yield inconsistent outputs due to poor accuracy and inconsistent quality, making it difficult to assess similarities between current and historical regimes for effective future predictions.

Innovation Solution

An interactive user interface and data framework utilizing models like large language, deep learning, and machine learning to generate reports and predictive outputs based on user queries, enabling automated analysis of historical data across regimes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional data analytic techniques are applied to large collections of historical data, then data analysis can be performed, but resource consumption increases and output consistency deteriorates

Engineering Contradiction:

Improvedata analysis accuracyVSAvoidresource consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The system performs preliminary actions by caching pre-generated reports for frequently queried parameters and date ranges. When a user submits a query, the system first checks if the report is already cached before executing the full data analysis, thereby reducing resource consumption while maintaining analysis accuracy.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies local quality by differentiating between cached reports (for common queries) and newly generated reports (for unique or modified queries). This selective approach ensures high accuracy for all reports while optimizing resource usage by avoiding redundant analysis of previously computed data.

Inventive Principle:

Principle #3Local quality

2Productivity

If conventional data analytic techniques are used on historical data with varying quality values, then data processing can proceed, but output consistency and reliability worsen

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidoutput consistency

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system implements feedback mechanisms by storing generated reports in a cache and using them to validate future analyses. When similar queries are made, the system compares new results against cached reports, ensuring output consistency and reliability even when processing data with varying quality values.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

By pre-generating and caching reports for standard queries, the system establishes a baseline of reliable outputs. This preliminary action creates a reference framework that ensures consistency across multiple data processing operations, reducing the impact of varying data quality on overall reliability.

Inventive Principle:

Principle #10Preliminary action

3Loss of information

If full data analysis is performed for every user query, then comprehensive insights are provided, but processing time and resource usage increase

Engineering Contradiction:

Improvedata insight completenessVSAvoidquery processing time

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The system performs preliminary data analysis and caches the results before users submit their queries. When a query is received, the system first checks the cache for existing reports, retrieving them instantly if available, thus providing comprehensive insights without the time penalty of full re-analysis.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

Instead of regenerating the same analysis multiple times, the system creates copies of previously generated reports and stores them in the cache. These copied reports are then served to users who submit similar queries, eliminating redundant processing while maintaining complete data insights.

Inventive Principle:

Principle #26Copying

4Adaptability or versatility

If historical data with poor accuracy is analyzed, then data processing can continue, but the ability to assess similarities between current and historical regimes deteriorates

Engineering Contradiction:

Improveregime comparison capabilityVSAvoiddata quality accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system uses feedback from cached reports to validate and correct analyses of historical data with poor accuracy. By comparing current regime analyses against cached historical reports, the system can identify and adjust for data quality issues, maintaining the ability to assess regime similarities reliably.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260064681A1Method and system for automating historical data analytics

Publication Date: 2026.03.05 JPMORGAN CHASE BANK NA

AI summary

A method for facilitating automated analysis of historical data is disclosed. The method includes generating a graphical user interface for a user, the graphical user interface including an interactive dashboard that is configured to receive input from the user; receiving, via the graphical user interface, queries from the user, the queries including parameters and date ranges; determining whether a report that corresponds to each of the queries is cached in a data repository; identifying, by using a model, data sets that correspond to the parameters and the date ranges when the report is not cached; generating, by using the model, a new report based on the identified data sets, the one new report corresponding to the queries; and displaying, via the graphical user interface, the new report for the user.